Models Fit

Strata 1

Species Female Adult Female Juvenile Male Adult Male Juvenile
Blacknose OK OK OK OK
Blacktip OK OK OK OK
Bull OK OK OK OK
Sandbar OK OK OK OK
Sharpnose OK OK OK OK
Spinner OK OK OK OK
Tiger OK OK OK OK

Strata 2

Species Female Adult Female Juvenile Male Adult Male Juvenile
Blacknose OK OK OK OK
Blacktip OK OK OK OK
Bull Failed Failed Failed Failed
Sandbar OK OK Failed OK
Sharpnose OK OK OK OK
Spinner Failed OK Failed OK
Tiger Failed OK Failed OK

The Primary Species I will Use are going to be Blacknose, Blacktip, and Sharpnose because they were fit for all levels of sex and maturity in both strata

Strata 1

Female

Adult

#### Strata 1 ####
  ## Female Adult
filenames_s1_FA <- list.files("output/output_GAM/strat1/Female_Adult", pattern="*.rda", full.names=TRUE)

for( i in 1:length( filenames_s1_FA ) ){ load( file = filenames_s1_FA[ i ] ) }

rm( filenames_s1_FA )

Blacknose

bn_s1_FA$results ### top 3 dAIC < 4
bn.top_s1_FA <- bn_s1_FA$model_fits$Lat_Lon_Root_Dep_Temp_Turb_Oxy_Sal

summary(bn.top_s1_FA)
## 
## Family: binomial 
## Link function: logit 
## 
## Formula:
## pres ~ s(startlon, startlat, k = k_i[[i]]["k_0", "Lat_Lon"] + 
##     35) + s(I(startdepth^0.25), k = k_i[[i]]["k_0", "Root_Dep"]) + 
##     s(tempbotm, k = k_i[[i]]["k_0", "Temp"]) + s(turbbotm, k = k_i[[i]]["k_0", 
##     "Turb"]) + s(oxybotm, k = k_i[[i]]["k_0", "Oxy"]) + s(salbotm, 
##     k = k_i[[i]]["k_0", "Sal"]) + s(year, bs = "re")
## 
## Parametric coefficients:
##             Estimate Std. Error z value Pr(>|z|)  
## (Intercept)   -5.851      2.911   -2.01   0.0445 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Approximate significance of smooth terms:
##                          edf Ref.df Chi.sq  p-value    
## s(startlon,startlat)  26.310 35.053 63.382  0.00229 ** 
## s(I(startdepth^0.25))  4.454  4.770 47.319 2.83e-09 ***
## s(tempbotm)            2.516  2.993 12.362  0.00680 ** 
## s(turbbotm)            1.000  1.000  0.845  0.35788    
## s(oxybotm)             4.231  5.112 10.960  0.05638 .  
## s(salbotm)             1.001  1.003  6.121  0.01351 *  
## s(year)                7.362 16.000 13.580  0.02096 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## R-sq.(adj) =  0.218   Deviance explained = 29.6%
## UBRE = -0.49073  Scale est. = 1         n = 2674

turbbotm not significant and edf = 1. salbotm p-value suggests significance but edf is approximatly equal to 1.

par(mfrow = c(2,2))
gam.check(bn.top_s1_FA)

## 
## Method: UBRE   Optimizer: outer newton
## full convergence after 7 iterations.
## Gradient range [-1.007571e-07,4.054833e-08]
## (score -0.4907316 & scale 1).
## Hessian positive definite, eigenvalue range [5.598889e-08,0.001193232].
## Model rank =  119 / 119 
## 
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
## 
##                          k'   edf k-index p-value    
## s(startlon,startlat)  74.00 26.31    0.92  <2e-16 ***
## s(I(startdepth^0.25))  7.00  4.45    0.98   0.710    
## s(tempbotm)            5.00  2.52    0.98   0.650    
## s(turbbotm)            4.00  1.00    0.98   0.510    
## s(oxybotm)             7.00  4.23    1.01   0.975    
## s(salbotm)             4.00  1.00    0.95   0.055 .  
## s(year)               17.00  7.36      NA      NA    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Plot of Residuals against linear predictor looks strange to me. The histogram of the residuals has a gap in it.

k` looks good for all variables.

par(mfrow = c(3,2))
#par(mfrow = c(1,1))

for(i in 2:7){
  
  plot(bn.top_s1_FA,
       select = i)
  abline(h = 0, col = "red", lty = 2)
  
}

root startdepth, turbbotm, are zero throughout their entire range

tempbotm, oxybotm maybe zero throughout it’s entire range.

salbotm looks linear and increasing very slightly.

I want to look at another model without the turbbotm smooth.

bn.2_s1_FA <- bn_s1_FA$model_fits$Lat_Lon_Root_Dep_Temp_Oxy_Sal

summary(bn.2_s1_FA)
## 
## Family: binomial 
## Link function: logit 
## 
## Formula:
## pres ~ s(startlon, startlat, k = k_i[[i]]["k_0", "Lat_Lon"] + 
##     35) + s(I(startdepth^0.25), k = k_i[[i]]["k_0", "Root_Dep"]) + 
##     s(tempbotm, k = k_i[[i]]["k_0", "Temp"]) + s(oxybotm, k = k_i[[i]]["k_0", 
##     "Oxy"]) + s(salbotm, k = k_i[[i]]["k_0", "Sal"]) + s(year, 
##     bs = "re")
## 
## Parametric coefficients:
##             Estimate Std. Error z value Pr(>|z|)  
## (Intercept)   -5.350      2.143  -2.497   0.0125 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Approximate significance of smooth terms:
##                          edf Ref.df Chi.sq  p-value    
## s(startlon,startlat)  21.038 28.007 63.793 0.000131 ***
## s(I(startdepth^0.25))  4.329  4.675 54.298  1.1e-10 ***
## s(tempbotm)            2.442  2.924 10.293 0.018946 *  
## s(oxybotm)             3.965  4.736 10.649 0.080266 .  
## s(salbotm)             1.001  1.002  6.608 0.010218 *  
## s(year)                7.715 16.000 15.602 0.009434 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## R-sq.(adj) =  0.211   Deviance explained = 28.4%
## UBRE = -0.48695  Scale est. = 1         n = 2737

salbotm edf is still near 1 but i think this is ok.

par(mfrow = c(2,2))
gam.check(bn.2_s1_FA)

## 
## Method: UBRE   Optimizer: outer newton
## full convergence after 7 iterations.
## Gradient range [-4.871036e-07,1.419548e-08]
## (score -0.4869459 & scale 1).
## Hessian positive definite, eigenvalue range [4.863151e-07,0.001296961].
## Model rank =  99 / 99 
## 
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
## 
##                          k'   edf k-index p-value    
## s(startlon,startlat)  59.00 21.04    0.92  <2e-16 ***
## s(I(startdepth^0.25))  7.00  4.33    0.99    0.83    
## s(tempbotm)            5.00  2.44    0.97    0.33    
## s(oxybotm)             6.00  3.97    1.00    0.94    
## s(salbotm)             4.00  1.00    0.94    0.07 .  
## s(year)               17.00  7.71      NA      NA    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

k` looks good for all variables

par(mfrow = c(2,2))
#par(mfrow = c(1,1))

for(i in 2:5){
  
  plot(bn.2_s1_FA,
       select = i)
  abline(h = 0, col = "red", lty = 2)
  
}

par(mfrow = c(1,1))
  plot(bn.2_s1_FA,
       select = 6)

root startdepth is zero throughout it’s entire range but is close near 2.5 and 3.5. tempbotm zero throughouts it’s entire range but it is very hard to tell near 18-19 or 27. Oxybotm Zero below 8 Positive 8-10, and zero above 10 (but wide CI). salbotm again appears linear and negative to left increasing to right (negative below approx. 32 and Pos above approx. 37.5 Zero between).

For good measure I want to look at the 2nd ranged model

bn.top2_s1_FA <- bn_s1_FA$model_fits$Lat_Lon_Root_Dep_Temp_Turb_Sal_Dis

summary(bn.top2_s1_FA)
## 
## Family: binomial 
## Link function: logit 
## 
## Formula:
## pres ~ s(startlon, startlat, k = k_i[[i]]["k_0", "Lat_Lon"] + 
##     35) + s(I(startdepth^0.25), k = k_i[[i]]["k_0", "Root_Dep"]) + 
##     s(tempbotm, k = k_i[[i]]["k_0", "Temp"]) + s(turbbotm, k = k_i[[i]]["k_0", 
##     "Turb"]) + s(salbotm, k = k_i[[i]]["k_0", "Sal"]) + s(Dis.to.SHORE, 
##     k = k_i[[i]]["k_0", "Dis"]) + s(year, bs = "re")
## 
## Parametric coefficients:
##             Estimate Std. Error z value Pr(>|z|)  
## (Intercept)   -7.091      4.069  -1.743   0.0814 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Approximate significance of smooth terms:
##                          edf Ref.df Chi.sq  p-value    
## s(startlon,startlat)  50.584 55.596 91.144  0.00152 ** 
## s(I(startdepth^0.25))  4.605  4.883 38.065 1.96e-07 ***
## s(tempbotm)            2.564  3.049 14.498  0.00257 ** 
## s(turbbotm)            1.000  1.000  0.677  0.41059    
## s(salbotm)             1.000  1.000  2.681  0.10159    
## s(Dis.to.SHORE)        1.003  1.006  3.357  0.06699 .  
## s(year)                8.868 16.000 19.758  0.00343 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## R-sq.(adj) =  0.225   Deviance explained = 31.8%
## UBRE = -0.48927  Scale est. = 1         n = 2677

edf of both turbbotm and salbotm is 1 and p-value is not significant.

par(mfrow = c(2,2))
gam.check(bn.top2_s1_FA)

## 
## Method: UBRE   Optimizer: outer newton
## full convergence after 8 iterations.
## Gradient range [-2.964092e-07,9.035072e-08]
## (score -0.489268 & scale 1).
## Hessian positive definite, eigenvalue range [1.13778e-08,0.001384957].
## Model rank =  101 / 101 
## 
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
## 
##                          k'   edf k-index p-value  
## s(startlon,startlat)  59.00 50.58    0.95    0.07 .
## s(I(startdepth^0.25))  7.00  4.60    0.99    0.85  
## s(tempbotm)            5.00  2.56    0.98    0.65  
## s(turbbotm)            4.00  1.00    0.98    0.64  
## s(salbotm)             4.00  1.00    0.94    0.06 .
## s(Dis.to.SHORE)        4.00  1.00    0.96    0.32  
## s(year)               17.00  8.87      NA      NA  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
par(mfrow = c(2,3))
#par(mfrow = c(1,1))

for(i in 2:6){
  
  plot(bn.top2_s1_FA,
       select = i)
  abline(h = 0, col = "red", lty = 2)
  
}

par(mfrow = c(1,1))

plot(bn.top2_s1_FA,
       select = 2)
abline(h = 0, col = "red", lty = 2)

plot(bn.top2_s1_FA,
       select = 3,
     ylim = c(-10,10))
abline(h = 0, col = "red", lty = 2)

plot(bn.top2_s1_FA,
       select = 4,
     ylim = c(-2,2))
abline(h = 0, col = "red", lty = 2)

plot(bn.top2_s1_FA,
       select = 5,
     ylim = c(-5,5))
abline(h = 0, col = "red", lty = 2)

plot(bn.top2_s1_FA,
       select = 6,
     ylim = c(-5,5))
abline(h = 0, col = "red", lty = 2)

plot(bn.top2_s1_FA,
       select = 7)
abline(h = 0, col = "red", lty = 2)

root startdepth is zero thoroughout it’s entire range.

tempbotm is positive approx. between 24 and 26 zero everywhere else.

turbbotm is zero everywhere

salbotm is zero everywhere

Dis.to.SHORE is zero everywhere

bn.top3_s1_FA <- bn_s1_FA$model_fits$Lat_Lon_Root_Dep_Temp_Turb_Oxy

summary(bn.top3_s1_FA)
## 
## Family: binomial 
## Link function: logit 
## 
## Formula:
## pres ~ s(startlon, startlat, k = k_i[[i]]["k_0", "Lat_Lon"] + 
##     35) + s(I(startdepth^0.25), k = k_i[[i]]["k_0", "Root_Dep"]) + 
##     s(tempbotm, k = k_i[[i]]["k_0", "Temp"]) + s(turbbotm, k = k_i[[i]]["k_0", 
##     "Turb"]) + s(oxybotm, k = k_i[[i]]["k_0", "Oxy"]) + s(year, 
##     bs = "re")
## 
## Parametric coefficients:
##             Estimate Std. Error z value Pr(>|z|)
## (Intercept)   -7.077      4.472  -1.582    0.114
## 
## Approximate significance of smooth terms:
##                          edf Ref.df  Chi.sq  p-value    
## s(startlon,startlat)  54.584 63.270 101.899  0.00143 ** 
## s(I(startdepth^0.25))  4.655  4.913  40.937 5.34e-08 ***
## s(tempbotm)            2.583  3.066  13.946  0.00349 ** 
## s(turbbotm)            1.000  1.000   0.186  0.66602    
## s(oxybotm)             3.930  4.796  10.537  0.07810 .  
## s(year)                8.385 16.000  16.538  0.01145 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## R-sq.(adj) =  0.233   Deviance explained = 32.6%
## UBRE = -0.48978  Scale est. = 1         n = 2674
gam.check(bn.top3_s1_FA)

## 
## Method: UBRE   Optimizer: outer newton
## full convergence after 9 iterations.
## Gradient range [-1.32645e-07,2.710248e-10]
## (score -0.4897841 & scale 1).
## Hessian positive definite, eigenvalue range [5.340866e-09,0.001257979].
## Model rank =  115 / 115 
## 
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
## 
##                          k'   edf k-index p-value  
## s(startlon,startlat)  74.00 54.58    0.95   0.085 .
## s(I(startdepth^0.25))  7.00  4.66    0.99   0.820  
## s(tempbotm)            5.00  2.58    0.99   0.735  
## s(turbbotm)            4.00  1.00    0.98   0.600  
## s(oxybotm)             7.00  3.93    1.01   0.960  
## s(year)               17.00  8.38      NA      NA  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot(bn.top3_s1_FA,pages = 1)

bn.top19_s1_FA <- bn_s1_FA$model_fits$Lat_Lon_Root_Dep_Temp_Oxy_Sal_Dis

summary(bn.top19_s1_FA)
## 
## Family: binomial 
## Link function: logit 
## 
## Formula:
## pres ~ s(startlon, startlat, k = k_i[[i]]["k_0", "Lat_Lon"] + 
##     35) + s(I(startdepth^0.25), k = k_i[[i]]["k_0", "Root_Dep"]) + 
##     s(tempbotm, k = k_i[[i]]["k_0", "Temp"]) + s(oxybotm, k = k_i[[i]]["k_0", 
##     "Oxy"]) + s(salbotm, k = k_i[[i]]["k_0", "Sal"]) + s(Dis.to.SHORE, 
##     k = k_i[[i]]["k_0", "Dis"]) + s(year, bs = "re")
## 
## Parametric coefficients:
##             Estimate Std. Error z value Pr(>|z|)  
## (Intercept)   -7.041      4.134  -1.703   0.0885 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Approximate significance of smooth terms:
##                          edf Ref.df Chi.sq  p-value    
## s(startlon,startlat)  49.780 55.101 97.407 0.000357 ***
## s(I(startdepth^0.25))  4.632  4.900 41.713 3.63e-08 ***
## s(tempbotm)            2.472  2.962 11.526 0.010695 *  
## s(oxybotm)             3.679  4.449  9.878 0.080628 .  
## s(salbotm)             1.000  1.000  3.244 0.071755 .  
## s(Dis.to.SHORE)        1.005  1.009  3.698 0.054533 .  
## s(year)                8.465 16.000 18.509 0.004210 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## R-sq.(adj) =  0.229   Deviance explained = 31.9%
## UBRE = -0.4879  Scale est. = 1         n = 2737
gam.check(bn.top19_s1_FA)

## 
## Method: UBRE   Optimizer: outer newton
## full convergence after 10 iterations.
## Gradient range [-3.494731e-07,1.19877e-07]
## (score -0.4879004 & scale 1).
## Hessian positive definite, eigenvalue range [1.322699e-07,0.001393325].
## Model rank =  103 / 103 
## 
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
## 
##                          k'   edf k-index p-value  
## s(startlon,startlat)  59.00 49.78    0.95    0.05 *
## s(I(startdepth^0.25))  7.00  4.63    1.00    0.88  
## s(tempbotm)            5.00  2.47    0.97    0.38  
## s(oxybotm)             6.00  3.68    1.00    0.95  
## s(salbotm)             4.00  1.00    0.94    0.05 *
## s(Dis.to.SHORE)        4.00  1.01    0.96    0.14  
## s(year)               17.00  8.47      NA      NA  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#par(mfrow = c(1,1))
plot(bn.top19_s1_FA,pages = 1)

plot(bn.top19_s1_FA,
     select = 6)
abline(h = 0, col = "red", lty = 2)

I think the best model to use is the one with the same structure as the top model but without the turbbotm smooth.

bn_s1_FA_best <- bn_s1_FA$model_fits$Lat_Lon_Root_Dep_Temp_Oxy_Sal

#-saves_8/22/18-#save(bn_s1_FA_best, file = "output/output_GAM/best_strat1/Female_Adult/bn_s1_FA_best.rda")

Blacktip

bt_s1_FA$results[c(1:20), c(1:4,7)]
bt.top_s1_FA <- bt_s1_FA$model_fits$Lat_Lon_Root_Dep_Temp_Turb_Oxy_Sal_Dis

summary(bt.top_s1_FA)
## 
## Family: binomial 
## Link function: logit 
## 
## Formula:
## pres ~ s(startlon, startlat, k = k_i[[i]]["k_0", "Lat_Lon"] + 
##     35) + s(I(startdepth^0.25), k = k_i[[i]]["k_0", "Root_Dep"]) + 
##     s(tempbotm, k = k_i[[i]]["k_0", "Temp"]) + s(turbbotm, k = k_i[[i]]["k_0", 
##     "Turb"]) + s(oxybotm, k = k_i[[i]]["k_0", "Oxy"]) + s(salbotm, 
##     k = k_i[[i]]["k_0", "Sal"]) + s(Dis.to.SHORE, k = k_i[[i]]["k_0", 
##     "Dis"]) + s(year, bs = "re")
## 
## Parametric coefficients:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept)   -4.512      0.443  -10.19   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Approximate significance of smooth terms:
##                          edf Ref.df Chi.sq  p-value    
## s(startlon,startlat)  25.613 31.420 66.771 0.000242 ***
## s(I(startdepth^0.25))  1.001  1.002  0.053 0.819924    
## s(tempbotm)            2.973  3.520 18.691 0.000637 ***
## s(turbbotm)            1.000  1.000  7.697 0.005535 ** 
## s(oxybotm)             3.557  4.176 10.221 0.041105 *  
## s(salbotm)             1.000  1.000  3.989 0.045807 *  
## s(Dis.to.SHORE)        2.052  2.510  4.981 0.230868    
## s(year)                8.183 16.000 15.766 0.015744 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## R-sq.(adj) =  0.292   Deviance explained = 38.1%
## UBRE = -0.63162  Scale est. = 1         n = 2674
par(mfrow = c(2,2))
gam.check(bt.top_s1_FA)

## 
## Method: UBRE   Optimizer: outer newton
## full convergence after 12 iterations.
## Gradient range [-4.255699e-08,1.451537e-07]
## (score -0.6316182 & scale 1).
## eigenvalue range [-1.447479e-07,0.001348041].
## Model rank =  89 / 89 
## 
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
## 
##                          k'   edf k-index p-value   
## s(startlon,startlat)  44.00 25.61    0.98   0.690   
## s(I(startdepth^0.25))  4.00  1.00    0.94   0.100 . 
## s(tempbotm)            5.00  2.97    0.96   0.310   
## s(turbbotm)            4.00  1.00    0.98   0.825   
## s(oxybotm)             5.00  3.56    0.92   0.005 **
## s(salbotm)             4.00  1.00    0.97   0.505   
## s(Dis.to.SHORE)        5.00  2.05    0.97   0.550   
## s(year)               17.00  8.18      NA      NA   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

depth, Turb, Sal edf all = 1.

plot(bt.top_s1_FA,
     pages = 1)

par(mfrow = c(2,3))

for(i in 2:7){
  
  plot(bt.top_s1_FA,
     select = i)
  abline(h = 0, col = "red", lty = 3)
  
}

startdepth is zero

turbbotm looks linear decreasing to zero near 75ish, negative past 80.

Salbotm is zero but if CI were closer would be linear increasing.

** Dis.to.SHORE** is zero

bt.2_s1_FA <- bt_s1_FA$model_fits$Lat_Lon_Temp_Turb_Oxy

summary(bt.2_s1_FA)
## 
## Family: binomial 
## Link function: logit 
## 
## Formula:
## pres ~ s(startlon, startlat, k = k_i[[i]]["k_0", "Lat_Lon"] + 
##     35) + s(tempbotm, k = k_i[[i]]["k_0", "Temp"]) + s(turbbotm, 
##     k = k_i[[i]]["k_0", "Turb"]) + s(oxybotm, k = k_i[[i]]["k_0", 
##     "Oxy"]) + s(year, bs = "re")
## 
## Parametric coefficients:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept)  -4.4967     0.4295  -10.47   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Approximate significance of smooth terms:
##                         edf Ref.df  Chi.sq  p-value    
## s(startlon,startlat) 27.426 33.258 101.681 7.69e-09 ***
## s(tempbotm)           2.937  3.483  22.591 0.000165 ***
## s(turbbotm)           1.006  1.011   6.968 0.008497 ** 
## s(oxybotm)            3.484  4.109   9.320 0.056524 .  
## s(year)               8.013 16.000  14.786 0.022647 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## R-sq.(adj) =  0.284   Deviance explained = 37.7%
## UBRE = -0.63122  Scale est. = 1         n = 2674
par(mfrow = c(2,2))
gam.check(bt.2_s1_FA)

## 
## Method: UBRE   Optimizer: outer newton
## full convergence after 8 iterations.
## Gradient range [-5.273813e-07,1.019381e-07]
## (score -0.6312196 & scale 1).
## Hessian positive definite, eigenvalue range [5.434824e-07,0.002039663].
## Model rank =  76 / 76 
## 
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
## 
##                         k'   edf k-index p-value   
## s(startlon,startlat) 44.00 27.43    0.98    0.80   
## s(tempbotm)           5.00  2.94    0.95    0.24   
## s(turbbotm)           4.00  1.01    0.98    0.81   
## s(oxybotm)            5.00  3.48    0.92    0.01 **
## s(year)              17.00  8.01      NA      NA   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
par(mfrow = c(2,2))

for(i in 2:5){
  
  plot(bt.2_s1_FA,
     select = i)
  abline(h = 0, col = "red", lty = 3)
  
}

anova.gam(bt.top_s1_FA, bt.2_s1_FA, test = "Chisq") ## No difference
AIC(bt.top_s1_FA, bt.2_s1_FA)
bt_s1_FA_best <- bt_s1_FA$model_fits$Lat_Lon_Temp_Turb_Oxy

#-saves_8/23/18-#save(bt_s1_FA_best, file = "output/output_GAM/best_strat1/Female_Adult/bt_s1_FA_best.rda")

Bull

bu_s1_FA$results[c(1:20), c(1:4,7)]

bu.top_s1_FA <- bu_s1_FA$model_fits$Lat_Lon_Root_Dep_Dis

summary(bu.top_s1_FA)

Sandbar

sb_s1_FA$results[c(1:20), c(1:4,7)]

sb.top_s1_FA <- sb_s1_FA$model_fits$Lat_Lon_Root_Dep_Temp_Turb_Sal_Dis

summary(sb.top_s1_FA)

Sharpnose

sn_s1_FA$results[c(1:20), c(1:4,7)]
sn.top_s1_FA <- sn_s1_FA$model_fits$Lat_Lon_Root_Dep_Temp_Turb_Oxy_Dis

summary(sn.top_s1_FA)
## 
## Family: binomial 
## Link function: logit 
## 
## Formula:
## pres ~ s(startlon, startlat, k = k_i[[i]]["k_0", "Lat_Lon"] + 
##     35) + s(I(startdepth^0.25), k = k_i[[i]]["k_0", "Root_Dep"]) + 
##     s(tempbotm, k = k_i[[i]]["k_0", "Temp"]) + s(turbbotm, k = k_i[[i]]["k_0", 
##     "Turb"]) + s(oxybotm, k = k_i[[i]]["k_0", "Oxy"]) + s(Dis.to.SHORE, 
##     k = k_i[[i]]["k_0", "Dis"]) + s(year, bs = "re")
## 
## Parametric coefficients:
##             Estimate Std. Error z value Pr(>|z|)  
## (Intercept)   -6.097      3.037  -2.008   0.0447 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Approximate significance of smooth terms:
##                          edf Ref.df  Chi.sq  p-value    
## s(startlon,startlat)  41.572 49.071 141.888 5.98e-11 ***
## s(I(startdepth^0.25))  4.961  5.143  84.240  < 2e-16 ***
## s(tempbotm)            3.663  4.311  57.536 3.65e-11 ***
## s(turbbotm)            7.088  7.733  17.299   0.0178 *  
## s(oxybotm)             1.000  1.000   4.627   0.0315 *  
## s(Dis.to.SHORE)        4.075  4.916   6.393   0.2148    
## s(year)               13.784 16.000  70.615 5.60e-11 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## R-sq.(adj) =  0.505   Deviance explained = 50.9%
## UBRE = -0.41467  Scale est. = 1         n = 2674

oxybotm edf = 1

par(mfrow = c(2,2))

gam.check(sn.top_s1_FA)

## 
## Method: UBRE   Optimizer: outer newton
## full convergence after 10 iterations.
## Gradient range [-2.023465e-08,1.433356e-09]
## (score -0.4146671 & scale 1).
## Hessian positive definite, eigenvalue range [2.023309e-08,0.001299258].
## Model rank =  109 / 109 
## 
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
## 
##                          k'   edf k-index p-value    
## s(startlon,startlat)  59.00 41.57    0.94  <2e-16 ***
## s(I(startdepth^0.25))  7.00  4.96    1.00    0.68    
## s(tempbotm)            6.00  3.66    0.99    0.42    
## s(turbbotm)            8.00  7.09    1.00    0.61    
## s(oxybotm)             4.00  1.00    0.97    0.08 .  
## s(Dis.to.SHORE)        7.00  4.08    0.98    0.26    
## s(year)               17.00 13.78      NA      NA    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
par(mfrow = c(2,3))
par(mfrow = c(1,1))

for(i in 2:7 ){
  
  plot( sn.top_s1_FA,
       select = i)
  abline(h = 0, col = "red", lty = 3)

  }

Spinner

sp_s1_FA$results[c(1:20), c(1:4,7)]

sp.top_s1_FA <- sp_s1_FA$model_fits$Lat_Lon_Root_Dep_Temp_Turb_Oxy_Dis

summary(sp.top_s1_FA)

Tiger

ti_s1_FA$results[c(1:20), c(1:4,7)] # top 8 dAIC < 4

ti.top_s1_FA <- ti_s1_FA$model_fits$Lat_Lon

summary(ti.top_s1_FA)

Juvenile

#### Strata 1 ####
  ## Female Juvenile
filenames_s1_FJ <- list.files("output/output_GAM/strat1/Female_Juvenile", pattern="*.rda", full.names=TRUE)

for( i in 1:length( filenames_s1_FJ ) ){ load( file = filenames_s1_FJ[ i ] ) }

rm( filenames_s1_FJ )

Blacknose

bn_s1_FJ$results[c(1:20), c(1:4,7)]
bn.top_s1_FJ <- bn_s1_FJ$model_fits$Lat_Lon_Root_Dep_Temp_Turb_Oxy_Sal

summary(bn.top_s1_FJ)
## 
## Family: binomial 
## Link function: logit 
## 
## Formula:
## pres ~ s(startlon, startlat, k = k_i[[i]]["k_0", "Lat_Lon"] + 
##     35) + s(I(startdepth^0.25), k = k_i[[i]]["k_0", "Root_Dep"]) + 
##     s(tempbotm, k = k_i[[i]]["k_0", "Temp"]) + s(turbbotm, k = k_i[[i]]["k_0", 
##     "Turb"]) + s(oxybotm, k = k_i[[i]]["k_0", "Oxy"]) + s(salbotm, 
##     k = k_i[[i]]["k_0", "Sal"]) + s(year, bs = "re")
## 
## Parametric coefficients:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept)   -5.800      1.361  -4.262 2.03e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Approximate significance of smooth terms:
##                          edf Ref.df Chi.sq p-value    
## s(startlon,startlat)  15.107 19.975 54.817 4.3e-05 ***
## s(I(startdepth^0.25))  2.276  2.627 10.020  0.0116 *  
## s(tempbotm)            3.063  3.368  4.882  0.3066    
## s(turbbotm)            2.485  3.052  6.722  0.0901 .  
## s(oxybotm)             1.001  1.002  3.221  0.0731 .  
## s(salbotm)             1.000  1.001  3.324  0.0684 .  
## s(year)                4.334 16.000  5.870  0.1511    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## R-sq.(adj) =  0.159   Deviance explained =   28%
## UBRE = -0.58364  Scale est. = 1         n = 2674
par(mfrow = c(2,2))

gam.check(bn.top_s1_FJ)

## 
## Method: UBRE   Optimizer: outer newton
## full convergence after 8 iterations.
## Gradient range [-4.489654e-07,2.932907e-07]
## (score -0.583635 & scale 1).
## Hessian positive definite, eigenvalue range [1.504241e-07,0.00143772].
## Model rank =  85 / 85 
## 
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
## 
##                          k'   edf k-index p-value   
## s(startlon,startlat)  44.00 15.11    0.93    0.01 **
## s(I(startdepth^0.25))  5.00  2.28    0.99    0.90   
## s(tempbotm)            5.00  3.06    0.99    0.85   
## s(turbbotm)            5.00  2.48    0.96    0.33   
## s(oxybotm)             4.00  1.00    0.97    0.44   
## s(salbotm)             4.00  1.00    0.95    0.08 . 
## s(year)               17.00  4.33      NA      NA   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
par(mfrow = c(1,1))

for(i in 2:7 ){
  
  plot( bn.top_s1_FJ,
       select = i)
  abline(h = 0, col = "red", lty = 3)

  }

Blacktip

bt_s1_FJ$results[c(1:20), c(1:4,7)]
bt.top_s1_FJ <- bt_s1_FJ$model_fits$Lat_Lon_Root_Dep_Temp_Turb_Oxy_Dis

summary(bt.top_s1_FJ)
## 
## Family: binomial 
## Link function: logit 
## 
## Formula:
## pres ~ s(startlon, startlat, k = k_i[[i]]["k_0", "Lat_Lon"] + 
##     35) + s(I(startdepth^0.25), k = k_i[[i]]["k_0", "Root_Dep"]) + 
##     s(tempbotm, k = k_i[[i]]["k_0", "Temp"]) + s(turbbotm, k = k_i[[i]]["k_0", 
##     "Turb"]) + s(oxybotm, k = k_i[[i]]["k_0", "Oxy"]) + s(Dis.to.SHORE, 
##     k = k_i[[i]]["k_0", "Dis"]) + s(year, bs = "re")
## 
## Parametric coefficients:
##             Estimate Std. Error z value Pr(>|z|)  
## (Intercept)  -12.111      5.722  -2.117   0.0343 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Approximate significance of smooth terms:
##                         edf Ref.df Chi.sq  p-value    
## s(startlon,startlat)  8.190 11.041 33.888  0.00035 ***
## s(I(startdepth^0.25)) 2.874  3.010 10.147  0.02140 *  
## s(tempbotm)           1.000  1.000  2.215  0.13662    
## s(turbbotm)           2.226  2.755 23.541 3.56e-05 ***
## s(oxybotm)            4.489  4.851 14.450  0.00923 ** 
## s(Dis.to.SHORE)       1.001  1.001  9.384  0.00219 ** 
## s(year)               7.372 16.000 15.093  0.01036 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## R-sq.(adj) =  0.337   Deviance explained = 45.7%
## UBRE = -0.72631  Scale est. = 1         n = 2674
par(mfrow = c(2,2))

gam.check(bt.top_s1_FJ)

## 
## Method: UBRE   Optimizer: outer newton
## full convergence after 8 iterations.
## Gradient range [-1.608715e-07,5.434471e-07]
## (score -0.7263134 & scale 1).
## Hessian positive definite, eigenvalue range [9.670948e-08,0.001158392].
## Model rank =  85 / 85 
## 
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
## 
##                          k'   edf k-index p-value    
## s(startlon,startlat)  44.00  8.19    0.99    0.66    
## s(I(startdepth^0.25))  5.00  2.87    0.99    0.78    
## s(tempbotm)            4.00  1.00    0.93  <2e-16 ***
## s(turbbotm)            5.00  2.23    0.98    0.47    
## s(oxybotm)             5.00  4.49    0.98    0.38    
## s(Dis.to.SHORE)        4.00  1.00    0.93  <2e-16 ***
## s(year)               17.00  7.37      NA      NA    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
par(mfrow = c(1,1))

for(i in 2:7 ){
  
  plot( bt.top_s1_FJ,
       select = i)
  abline(h = 0, col = "red", lty = 3)

  }

Bull

bu_s1_FJ$results[c(1:20), c(1:4,7)]

bu.top_s1_FJ <- bu_s1_FJ$model_fits$Lat_Lon_Root_Dep_Turb

summary(bu.top_s1_FJ)

Sandbar

sb_s1_FJ$results[c(1:20), c(1:4,7)]

sb.top_s1_FJ <- sb_s1_FJ$model_fits$Lat_Lon_Root_Dep_Temp_Turb

summary(sb.top_s1_FJ)

Sharpnose

sn_s1_FJ$results[c(1:20), c(1:4,7)]
sn.top_s1_FJ <- sn_s1_FJ$model_fits$Lat_Lon_Root_Dep_Temp_Turb_Oxy

summary(sn.top_s1_FJ)
## 
## Family: binomial 
## Link function: logit 
## 
## Formula:
## pres ~ s(startlon, startlat, k = k_i[[i]]["k_0", "Lat_Lon"] + 
##     35) + s(I(startdepth^0.25), k = k_i[[i]]["k_0", "Root_Dep"]) + 
##     s(tempbotm, k = k_i[[i]]["k_0", "Temp"]) + s(turbbotm, k = k_i[[i]]["k_0", 
##     "Turb"]) + s(oxybotm, k = k_i[[i]]["k_0", "Oxy"]) + s(year, 
##     bs = "re")
## 
## Parametric coefficients:
##             Estimate Std. Error z value Pr(>|z|)  
## (Intercept)   -8.135      3.860  -2.107   0.0351 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Approximate significance of smooth terms:
##                          edf Ref.df  Chi.sq  p-value    
## s(startlon,startlat)  29.832 36.993 152.043 6.89e-16 ***
## s(I(startdepth^0.25))  4.741  4.952   8.520  0.15892    
## s(tempbotm)            3.948  4.411  17.102  0.00151 ** 
## s(turbbotm)            1.841  2.282   3.251  0.21007    
## s(oxybotm)             1.786  2.240   1.569  0.46196    
## s(year)               10.446 16.000  46.521 7.64e-09 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## R-sq.(adj) =  0.211   Deviance explained = 27.1%
## UBRE = -0.4506  Scale est. = 1         n = 2674
par(mfrow = c(2,2))

gam.check(sn.top_s1_FJ)

## 
## Method: UBRE   Optimizer: outer newton
## full convergence after 9 iterations.
## Gradient range [-2.809599e-09,2.850101e-09]
## (score -0.450598 & scale 1).
## Hessian positive definite, eigenvalue range [8.498759e-05,0.00193132].
## Model rank =  87 / 87 
## 
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
## 
##                          k'   edf k-index p-value    
## s(startlon,startlat)  49.00 29.83    0.92  <2e-16 ***
## s(I(startdepth^0.25))  6.00  4.74    0.98   0.620    
## s(tempbotm)            6.00  3.95    0.93   0.015 *  
## s(turbbotm)            4.00  1.84    0.97   0.565    
## s(oxybotm)             4.00  1.79    0.97   0.485    
## s(year)               17.00 10.45      NA      NA    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
par(mfrow = c(1,1))

for(i in 2:6 ){
  
  plot( sn.top_s1_FJ,
       select = i)
  abline(h = 0, col = "red", lty = 3)

  }

Spinner

sp_s1_FJ$results[c(1:20), c(1:4,7)]

sp.top_s1_FJ <- sp_s1_FJ$model_fits$Lat_Lon_Root_Dep_Temp_Turb_Oxy_Sal_Dis

summary(sp.top_s1_FJ)

Tiger

ti_s1_FJ$results[c(1:20), c(1:4,7)]

ti.top_s1_FJ <- ti_s1_FJ$model_fits$Lat_Lon_Root_Dep_Turb_Sal_Dis

summary(ti.top_s1_FJ)

Male

Adult

#### Strata 1 ####
  ## Male Adult
filenames_s1_MA <- list.files("output/output_GAM/strat1/Male_Adult", pattern="*.rda", full.names=TRUE)

for( i in 1:length( filenames_s1_MA ) ){ load( file = filenames_s1_MA[ i ] ) }

rm( filenames_s1_MA )

Blacknose

bn_s1_MA$results[c(1:20), c(1:4,7)]
bn.top_s1_MA <- bn_s1_MA$model_fits$Lat_Lon_Root_Dep_Temp_Turb_Oxy_Sal_Dis

summary(bn.top_s1_MA)
## 
## Family: binomial 
## Link function: logit 
## 
## Formula:
## pres ~ s(startlon, startlat, k = k_i[[i]]["k_0", "Lat_Lon"] + 
##     35) + s(I(startdepth^0.25), k = k_i[[i]]["k_0", "Root_Dep"]) + 
##     s(tempbotm, k = k_i[[i]]["k_0", "Temp"]) + s(turbbotm, k = k_i[[i]]["k_0", 
##     "Turb"]) + s(oxybotm, k = k_i[[i]]["k_0", "Oxy"]) + s(salbotm, 
##     k = k_i[[i]]["k_0", "Sal"]) + s(Dis.to.SHORE, k = k_i[[i]]["k_0", 
##     "Dis"]) + s(year, bs = "re")
## 
## Parametric coefficients:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept)   -5.741      1.708  -3.361 0.000777 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Approximate significance of smooth terms:
##                          edf Ref.df  Chi.sq  p-value    
## s(startlon,startlat)  43.658 51.687 156.605 2.03e-12 ***
## s(I(startdepth^0.25))  2.698  3.126  16.497 0.000625 ***
## s(tempbotm)            3.916  4.303   2.939 0.520959    
## s(turbbotm)            1.891  2.351   3.736 0.243220    
## s(oxybotm)             1.937  2.418   3.959 0.155846    
## s(salbotm)             2.272  2.847   7.871 0.034564 *  
## s(Dis.to.SHORE)        1.001  1.002   6.645 0.009954 ** 
## s(year)               12.845 16.000  50.818 1.03e-07 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## R-sq.(adj) =   0.35   Deviance explained = 41.7%
## UBRE = -0.5446  Scale est. = 1         n = 2674
par(mfrow = c(2,2))

gam.check(bn.top_s1_MA)

## 
## Method: UBRE   Optimizer: outer newton
## full convergence after 9 iterations.
## Gradient range [-4.722249e-07,1.165331e-07]
## (score -0.5446026 & scale 1).
## Hessian positive definite, eigenvalue range [4.717357e-07,0.00109428].
## Model rank =  111 / 111 
## 
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
## 
##                          k'   edf k-index p-value  
## s(startlon,startlat)  64.00 43.66    0.99   0.745  
## s(I(startdepth^0.25))  6.00  2.70    0.95   0.065 .
## s(tempbotm)            6.00  3.92    0.99   0.680  
## s(turbbotm)            4.00  1.89    1.03   0.990  
## s(oxybotm)             4.00  1.94    0.98   0.500  
## s(salbotm)             5.00  2.27    0.98   0.490  
## s(Dis.to.SHORE)        4.00  1.00    0.99   0.750  
## s(year)               17.00 12.84      NA      NA  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
par(mfrow = c(1,1))

for(i in 2:8 ){
  
  plot( bn.top_s1_MA,
       select = i)
  abline(h = 0, col = "red", lty = 3)

  }

Blacktip

bt_s1_MA$results[c(1:20), c(1:4,7)]
bt.top_s1_MA <- bt_s1_MA$model_fits$Lat_Lon_Root_Dep_Temp_Turb_Oxy_Dis

summary(bt.top_s1_MA)
## 
## Family: binomial 
## Link function: logit 
## 
## Formula:
## pres ~ s(startlon, startlat, k = k_i[[i]]["k_0", "Lat_Lon"] + 
##     35) + s(I(startdepth^0.25), k = k_i[[i]]["k_0", "Root_Dep"]) + 
##     s(tempbotm, k = k_i[[i]]["k_0", "Temp"]) + s(turbbotm, k = k_i[[i]]["k_0", 
##     "Turb"]) + s(oxybotm, k = k_i[[i]]["k_0", "Oxy"]) + s(Dis.to.SHORE, 
##     k = k_i[[i]]["k_0", "Dis"]) + s(year, bs = "re")
## 
## Parametric coefficients:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept)   -5.244      0.731  -7.174 7.31e-13 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Approximate significance of smooth terms:
##                          edf Ref.df Chi.sq  p-value    
## s(startlon,startlat)  25.118 31.000 68.536 0.000118 ***
## s(I(startdepth^0.25))  1.931  2.272  7.742 0.070878 .  
## s(tempbotm)            2.885  3.322 10.974 0.013612 *  
## s(turbbotm)            3.720  4.302 13.968 0.009262 ** 
## s(oxybotm)             3.142  3.764 11.485 0.019681 *  
## s(Dis.to.SHORE)        1.002  1.004  3.801 0.051286 .  
## s(year)                5.172 16.000  8.705 0.045300 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## R-sq.(adj) =  0.226   Deviance explained = 35.4%
## UBRE = -0.63944  Scale est. = 1         n = 2674
par(mfrow = c(2,2))

gam.check(bt.top_s1_MA)

## 
## Method: UBRE   Optimizer: outer newton
## full convergence after 6 iterations.
## Gradient range [-1.383859e-07,3.438899e-07]
## (score -0.639438 & scale 1).
## Hessian positive definite, eigenvalue range [1.371934e-07,0.0009193978].
## Model rank =  85 / 85 
## 
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
## 
##                          k'   edf k-index p-value    
## s(startlon,startlat)  44.00 25.12    0.92  <2e-16 ***
## s(I(startdepth^0.25))  4.00  1.93    0.95    0.17    
## s(tempbotm)            5.00  2.88    0.96    0.21    
## s(turbbotm)            5.00  3.72    0.96    0.35    
## s(oxybotm)             5.00  3.14    0.97    0.57    
## s(Dis.to.SHORE)        4.00  1.00    0.97    0.61    
## s(year)               17.00  5.17      NA      NA    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
par(mfrow = c(1,1))

for(i in 2:7 ){
  
  plot( bt.top_s1_MA,
       select = i)
  abline(h = 0, col = "red", lty = 3)

  }

Bull

bu_s1_MA$results[c(1:20), c(1:4,7)]

bu.top_s1_MA <- bu_s1_MA$model_fits$Lat_Lon_Root_Dep_Turb_Dis

summary(bu.top_s1_MA)

WOW!!! lol only 1 male Adult Bull shark…

Sandbar

sb_s1_MA$results[c(1:20), c(1:4,7)]

sb.top_s1_MA <- sb_s1_MA$model_fits$Lat_Lon_Root_Dep_Temp_Turb_Oxy_Dis

summary(sb.top_s1_MA)

Sharpnose

sn_s1_MA$results[c(1:20), c(1:4,7)]
sn.top_s1_MA <- sn_s1_MA$model_fits$Lat_Lon_Root_Dep_Temp_Turb_Oxy_Sal

summary(sn.top_s1_MA)
## 
## Family: binomial 
## Link function: logit 
## 
## Formula:
## pres ~ s(startlon, startlat, k = k_i[[i]]["k_0", "Lat_Lon"] + 
##     35) + s(I(startdepth^0.25), k = k_i[[i]]["k_0", "Root_Dep"]) + 
##     s(tempbotm, k = k_i[[i]]["k_0", "Temp"]) + s(turbbotm, k = k_i[[i]]["k_0", 
##     "Turb"]) + s(oxybotm, k = k_i[[i]]["k_0", "Oxy"]) + s(salbotm, 
##     k = k_i[[i]]["k_0", "Sal"]) + s(year, bs = "re")
## 
## Parametric coefficients:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept)   -2.836      0.602  -4.711 2.47e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Approximate significance of smooth terms:
##                          edf Ref.df  Chi.sq  p-value    
## s(startlon,startlat)  40.195 45.403 286.450  < 2e-16 ***
## s(I(startdepth^0.25))  4.359  4.793  83.561 1.07e-13 ***
## s(tempbotm)            3.195  3.888  30.827 6.69e-06 ***
## s(turbbotm)            4.250  5.015  15.728  0.00772 ** 
## s(oxybotm)             1.000  1.000   1.289  0.25627    
## s(salbotm)             2.265  2.850   5.419  0.11297    
## s(year)               14.838 16.000 117.195  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## R-sq.(adj) =  0.489   Deviance explained = 46.8%
## UBRE = -0.28583  Scale est. = 1         n = 2674
par(mfrow = c(2,2))

gam.check(sn.top_s1_MA)

## 
## Method: UBRE   Optimizer: outer newton
## full convergence after 8 iterations.
## Gradient range [-3.922849e-08,7.417563e-08]
## (score -0.2858262 & scale 1).
## Hessian positive definite, eigenvalue range [3.922582e-08,0.002001141].
## Model rank =  94 / 94 
## 
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
## 
##                          k'   edf k-index p-value    
## s(startlon,startlat)  49.00 40.20    0.95  <2e-16 ***
## s(I(startdepth^0.25))  6.00  4.36    0.96    0.02 *  
## s(tempbotm)            6.00  3.20    1.02    0.92    
## s(turbbotm)            6.00  4.25    1.00    0.60    
## s(oxybotm)             4.00  1.00    0.98    0.20    
## s(salbotm)             5.00  2.26    1.01    0.73    
## s(year)               17.00 14.84      NA      NA    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
par(mfrow = c(1,1))

for(i in 2:7 ){
  
  plot( sn.top_s1_MA,
       select = i)
  abline(h = 0, col = "red", lty = 3)

  }

Spinner

sp_s1_MA$results[c(1:20), c(1:4,7)]

sp.top_s1_MA <- sp_s1_MA$model_fits$Lat_Lon_Root_Dep_Temp_Turb_Oxy_Sal_Dis

summary(sp.top_s1_MA)

WOW!!! Unlikely

Tiger

ti_s1_MA$results[c(1:20), c(1:4,7)]

ti.top_s1_MA <- ti_s1_MA$model_fits$Lat_Lon_Dis

summary(ti.top_s1_MA)

WOW!!! Unlikely

Juvenile

#### Strata 1 ####
  ## Male Juvenile
filenames_s1_MJ <- list.files("output/output_GAM/strat1/Male_Juvenile", pattern="*.rda", full.names=TRUE)

for( i in 1:length( filenames_s1_MJ ) ){ load( file = filenames_s1_MJ[ i ] ) }

rm( filenames_s1_MJ )

Blacknose

bn_s1_MJ$results[c(1:20), c(1:4,7)]
bn.top_s1_MJ <- bn_s1_MJ$model_fits$Lat_Lon_Root_Dep_Temp_Turb_Oxy_Dis

summary(bn.top_s1_MJ)
## 
## Family: binomial 
## Link function: logit 
## 
## Formula:
## pres ~ s(startlon, startlat, k = k_i[[i]]["k_0", "Lat_Lon"] + 
##     35) + s(I(startdepth^0.25), k = k_i[[i]]["k_0", "Root_Dep"]) + 
##     s(tempbotm, k = k_i[[i]]["k_0", "Temp"]) + s(turbbotm, k = k_i[[i]]["k_0", 
##     "Turb"]) + s(oxybotm, k = k_i[[i]]["k_0", "Oxy"]) + s(Dis.to.SHORE, 
##     k = k_i[[i]]["k_0", "Dis"]) + s(year, bs = "re")
## 
## Parametric coefficients:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept)  -4.6669     0.6013  -7.761 8.41e-15 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Approximate significance of smooth terms:
##                          edf Ref.df Chi.sq  p-value    
## s(startlon,startlat)  24.297 30.679 68.024 0.000135 ***
## s(I(startdepth^0.25))  2.200  2.613 17.336 0.013000 *  
## s(tempbotm)            1.011  1.020  4.080 0.043679 *  
## s(turbbotm)            1.913  2.361  4.888 0.121610    
## s(oxybotm)             2.058  2.577  5.084 0.094590 .  
## s(Dis.to.SHORE)        2.055  2.506  7.593 0.060177 .  
## s(year)               10.739 16.000 25.077 0.001680 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## R-sq.(adj) =  0.222   Deviance explained = 32.6%
## UBRE = -0.56125  Scale est. = 1         n = 2674
par(mfrow = c(2,2))

gam.check(bn.top_s1_MJ)

## 
## Method: UBRE   Optimizer: outer newton
## full convergence after 8 iterations.
## Gradient range [-1.568556e-06,6.2321e-07]
## (score -0.5612491 & scale 1).
## Hessian positive definite, eigenvalue range [6.485626e-07,0.001140406].
## Model rank =  90 / 90 
## 
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
## 
##                          k'   edf k-index p-value
## s(startlon,startlat)  49.00 24.30    0.96    0.18
## s(I(startdepth^0.25))  5.00  2.20    1.00    0.96
## s(tempbotm)            4.00  1.01    0.97    0.38
## s(turbbotm)            4.00  1.91    0.98    0.63
## s(oxybotm)             5.00  2.06    0.97    0.37
## s(Dis.to.SHORE)        5.00  2.06    0.96    0.26
## s(year)               17.00 10.74      NA      NA
par(mfrow = c(1,1))

for(i in 2:7 ){
  
  plot( bn.top_s1_MJ,
       select = i)
  abline(h = 0, col = "red", lty = 3)

  }

Blacktip

bt_s1_MJ$results[c(1:20), c(1:4,7)]
bt.top_s1_MJ <- bt_s1_MJ$model_fits$Lat_Lon_Root_Dep_Temp_Turb_Oxy_Sal_Dis

summary(bt.top_s1_MJ)
## 
## Family: binomial 
## Link function: logit 
## 
## Formula:
## pres ~ s(startlon, startlat, k = k_i[[i]]["k_0", "Lat_Lon"] + 
##     35) + s(I(startdepth^0.25), k = k_i[[i]]["k_0", "Root_Dep"]) + 
##     s(tempbotm, k = k_i[[i]]["k_0", "Temp"]) + s(turbbotm, k = k_i[[i]]["k_0", 
##     "Turb"]) + s(oxybotm, k = k_i[[i]]["k_0", "Oxy"]) + s(salbotm, 
##     k = k_i[[i]]["k_0", "Sal"]) + s(Dis.to.SHORE, k = k_i[[i]]["k_0", 
##     "Dis"]) + s(year, bs = "re")
## 
## Parametric coefficients:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept)  -23.770      6.796  -3.498 0.000469 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Approximate significance of smooth terms:
##                          edf Ref.df Chi.sq p-value   
## s(startlon,startlat)  57.990 58.734 87.719 0.00991 **
## s(I(startdepth^0.25))  1.388  1.653  0.681 0.65426   
## s(tempbotm)            3.834  4.080  4.104 0.37735   
## s(turbbotm)            2.673  3.263 14.216 0.00380 **
## s(oxybotm)             3.748  4.565  5.743 0.29527   
## s(salbotm)             2.969  3.658  9.573 0.03729 * 
## s(Dis.to.SHORE)        1.000  1.001  1.704 0.19182   
## s(year)                9.154 16.000 17.605 0.01516 * 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## R-sq.(adj) =  0.365   Deviance explained = 49.7%
## UBRE = -0.71683  Scale est. = 1         n = 2674
par(mfrow = c(2,2))

gam.check(bt.top_s1_MJ)

## 
## Method: UBRE   Optimizer: outer newton
## full convergence after 11 iterations.
## Gradient range [-1.213233e-07,8.19273e-07]
## (score -0.7168305 & scale 1).
## Hessian positive definite, eigenvalue range [1.21295e-07,0.0009143236].
## Model rank =  109 / 109 
## 
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
## 
##                          k'   edf k-index p-value  
## s(startlon,startlat)  59.00 57.99    1.04   0.995  
## s(I(startdepth^0.25))  5.00  1.39    0.96   0.105  
## s(tempbotm)            6.00  3.83    0.96   0.075 .
## s(turbbotm)            5.00  2.67    0.98   0.455  
## s(oxybotm)             7.00  3.75    0.98   0.385  
## s(salbotm)             5.00  2.97    0.99   0.585  
## s(Dis.to.SHORE)        4.00  1.00    0.96   0.095 .
## s(year)               17.00  9.15      NA      NA  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
par(mfrow = c(1,1))

for(i in 2:8 ){
  
  plot( bt.top_s1_MJ,
       select = i)
  abline(h = 0, col = "red", lty = 3)

  }

Bull

bu_s1_MJ$results[c(1:20), c(1:4,7)]

bu.top_s1_MJ <- bu_s1_MJ$model_fits$Lat_Lon_Root_Dep_Turb_Oxy_Sal

summary(bu.top_s1_MJ)

Sandbar

sb_s1_MJ$results[c(1:20), c(1:4,7)] ### Top 7 dAIC < 4
### Lat_Lon in Model 4

sb.top_s1_MJ <- sb_s1_MJ$model_fits$Root_Dep_Temp_Turb_Oxy_Sal_Dis

summary(sb.top_s1_MJ)


sb.top4_s1_MJ <- sb_s1_MJ$model_fits$Lat_Lon_Root_Dep_Temp_Turb_Oxy_Sal

summary(sb.top4_s1_MJ)

Sharpnose

sn_s1_MJ$results[c(1:20), c(1:4,7)]
sn.top_s1_MJ <- sn_s1_MJ$model_fits$Lat_Lon_Root_Dep_Temp_Turb_Oxy_Sal

summary(sn.top_s1_MJ)
## 
## Family: binomial 
## Link function: logit 
## 
## Formula:
## pres ~ s(startlon, startlat, k = k_i[[i]]["k_0", "Lat_Lon"] + 
##     35) + s(I(startdepth^0.25), k = k_i[[i]]["k_0", "Root_Dep"]) + 
##     s(tempbotm, k = k_i[[i]]["k_0", "Temp"]) + s(turbbotm, k = k_i[[i]]["k_0", 
##     "Turb"]) + s(oxybotm, k = k_i[[i]]["k_0", "Oxy"]) + s(salbotm, 
##     k = k_i[[i]]["k_0", "Sal"]) + s(year, bs = "re")
## 
## Parametric coefficients:
##             Estimate Std. Error z value Pr(>|z|)   
## (Intercept)   -7.893      2.838  -2.781  0.00542 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Approximate significance of smooth terms:
##                          edf Ref.df Chi.sq  p-value    
## s(startlon,startlat)  27.452 33.724 98.341 3.21e-08 ***
## s(I(startdepth^0.25))  3.893  4.008  5.594  0.24735    
## s(tempbotm)            3.356  3.762 14.844  0.00227 ** 
## s(turbbotm)            1.053  1.101  1.304  0.26205    
## s(oxybotm)             1.727  2.165  3.367  0.23828    
## s(salbotm)             1.002  1.003  2.020  0.15627    
## s(year)                9.660 16.000 35.603 1.16e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## R-sq.(adj) =  0.178   Deviance explained = 26.3%
## UBRE = -0.46335  Scale est. = 1         n = 2674
par(mfrow = c(2,2))

gam.check(sn.top_s1_MJ)

## 
## Method: UBRE   Optimizer: outer newton
## full convergence after 9 iterations.
## Gradient range [-2.562695e-07,1.622538e-07]
## (score -0.4633548 & scale 1).
## Hessian positive definite, eigenvalue range [2.565911e-07,0.001930472].
## Model rank =  84 / 84 
## 
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
## 
##                          k'   edf k-index p-value   
## s(startlon,startlat)  44.00 27.45    0.92   0.005 **
## s(I(startdepth^0.25))  5.00  3.89    0.98   0.650   
## s(tempbotm)            5.00  3.36    0.95   0.135   
## s(turbbotm)            4.00  1.05    0.95   0.100 . 
## s(oxybotm)             4.00  1.73    0.98   0.640   
## s(salbotm)             4.00  1.00    0.93   0.025 * 
## s(year)               17.00  9.66      NA      NA   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
par(mfrow = c(1,1))

for(i in 2:7 ){
  
  plot( sn.top_s1_MJ,
       select = i)
  abline(h = 0, col = "red", lty = 3)

  }

Spinner

sp_s1_MJ$results[c(1:20), c(1:4,7)]

sp.top_s1_MJ <- sp_s1_MJ$model_fits$Lat_Lon_Root_Dep_Temp_Turb_Oxy_Sal_Dis

summary(sp.top_s1_MJ)

Tiger

ti_s1_MJ$results[c(1:20), c(1:4,7)] ### Top 21 dAIC < 4

ti.top_s1_MJ <- ti_s1_MJ$model_fits$Lat_Lon_Root_Dep_Turb

summary(ti.top_s1_MJ)

Strata 2

Female

Adult

#### Strata 2 ####
  ## Female Adult
filenames_s2_FA <- list.files("output/output_GAM/strat2/Female_Adult", pattern="*.rda", full.names=TRUE)

for( i in 1:length( filenames_s2_FA ) ){ load( file = filenames_s2_FA[ i ] ) }

rm( filenames_s2_FA )

Blacknose

bn_s2_FA$results[c(1:20), c(1:4,7)]
bn.top.s2_FA <- bn_s2_FA$model_fits$Lat_Lon_Turb_Dis

summary(bn.top.s2_FA)
## 
## Family: Gamma 
## Link function: log 
## 
## Formula:
## (CPUE + 1) ~ s(startlon, startlat, k = (k_i[[i]]["k_0", "Lat_Lon"] + 
##     35)) + s(turbbotm, k = k_i[[i]]["k_0", "Turb"]) + s(Dis.to.SHORE, 
##     k = k_i[[i]]["k_0", "Dis"]) + s(year, bs = "re")
## 
## Parametric coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   1.0227     0.0273   37.46   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Approximate significance of smooth terms:
##                            edf Ref.df     F p-value
## s(startlon,startlat) 5.079e+01  64.55 1.196   0.173
## s(turbbotm)          1.000e+00   1.00 1.017   0.314
## s(Dis.to.SHORE)      1.000e+00   1.00 0.186   0.667
## s(year)              4.645e-04  16.00 0.000   0.775
## 
## R-sq.(adj) =  0.115   Deviance explained = 40.7%
## GCV = 0.2019  Scale est. = 0.20939   n = 281
par(mfrow = c(2,2))

gam.check(bn.top.s2_FA)

## 
## Method: GCV   Optimizer: outer newton
## full convergence after 10 iterations.
## Gradient range [-1.845622e-07,-4.999857e-09]
## (score 0.2019046 & scale 0.2093924).
## Hessian positive definite, eigenvalue range [4.999853e-09,0.007194432].
## Model rank =  110 / 110 
## 
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
## 
##                            k'      edf k-index p-value
## s(startlon,startlat) 8.40e+01 5.08e+01    1.23    1.00
## s(turbbotm)          4.00e+00 1.00e+00    0.98    0.47
## s(Dis.to.SHORE)      4.00e+00 1.00e+00    1.05    0.88
## s(year)              1.70e+01 4.64e-04      NA      NA
par(mfrow = c(1,1))

for(i in 2:4 ){
  
  plot( bn.top.s2_FA,
       select = i)
  abline(h = 0, col = "red", lty = 3)

  }

Blacktip

bt_s2_FA$results
bt.top.s2_FA <- bt_s2_FA$model_fits$Lat_Lon_Turb_Oxy_Sal_Dis_Bottm

summary(bt.top.s2_FA)
## 
## Family: Gamma 
## Link function: log 
## 
## Formula:
## (CPUE + 1) ~ s(startlon, startlat, k = (k_i[[i]]["k_0", "Lat_Lon"] + 
##     35)) + s(turbbotm, k = k_i[[i]]["k_0", "Turb"]) + s(oxybotm, 
##     k = k_i[[i]]["k_0", "Oxy"]) + s(salbotm, k = k_i[[i]]["k_0", 
##     "Sal"]) + s(Dis.to.SHORE, k = k_i[[i]]["k_0", "Dis"]) + bottype4 + 
##     s(year, bs = "re")
## 
## Parametric coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)    0.8556     0.2279   3.754 0.000242 ***
## bottype4Mud    0.1670     0.2420   0.690 0.491149    
## bottype4Rock   0.1743     0.4694   0.371 0.710818    
## bottype4Sand   0.1603     0.2274   0.705 0.482036    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Approximate significance of smooth terms:
##                            edf Ref.df     F p-value
## s(startlon,startlat) 3.057e+01 38.343 0.831   0.761
## s(turbbotm)          1.222e+00  1.397 0.036   0.900
## s(oxybotm)           1.000e+00  1.000 1.645   0.201
## s(salbotm)           1.824e+00  2.261 1.267   0.263
## s(Dis.to.SHORE)      1.000e+00  1.000 0.084   0.772
## s(year)              3.323e-04 16.000 0.000   0.695
## 
## R-sq.(adj) =  0.125   Deviance explained =   46%
## GCV = 0.20452  Scale est. = 0.22593   n = 201
par(mfrow = c(2,2))

gam.check(bt.top.s2_FA)

## 
## Method: GCV   Optimizer: outer newton
## full convergence after 11 iterations.
## Gradient range [-7.937647e-08,-6.98817e-09]
## (score 0.2045176 & scale 0.2259285).
## Hessian positive definite, eigenvalue range [6.988093e-09,0.005753767].
## Model rank =  87 / 87 
## 
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
## 
##                            k'      edf k-index p-value
## s(startlon,startlat) 4.90e+01 3.06e+01    1.19    1.00
## s(turbbotm)          4.00e+00 1.22e+00    0.98    0.43
## s(oxybotm)           4.00e+00 1.00e+00    1.10    0.94
## s(salbotm)           5.00e+00 1.82e+00    1.04    0.88
## s(Dis.to.SHORE)      4.00e+00 1.00e+00    1.07    0.88
## s(year)              1.70e+01 3.32e-04      NA      NA
par(mfrow = c(1,1))

for(i in 2:7 ){
  
  plot( bt.top.s2_FA,
       select = i)
  abline(h = 0, col = "red", lty = 3)

  }

Bull

FAILED to fit model

bu_s2_FA$results

bu.top.s2_FA <- bu_s2_FA$model_fits

summary(bu.top.s2_FA)

Sandbar

sb_s2_FA$results

sb.top.s2_FA <- sb_s2_FA$model_fits$Lat_Lon_Temp_Turb_Dis

summary(sb.top.s2_FA)

Sharpnose

sn_s2_FA$results
sn.top.s2_FA <- sn_s2_FA$model_fits$Lat_Lon_Temp_Turb_Sal

summary(sn.top.s2_FA)
## 
## Family: Gamma 
## Link function: log 
## 
## Formula:
## (CPUE + 1) ~ s(startlon, startlat, k = (k_i[[i]]["k_0", "Lat_Lon"] + 
##     35)) + s(tempbotm, k = k_i[[i]]["k_0", "Temp"]) + s(turbbotm, 
##     k = k_i[[i]]["k_0", "Turb"]) + s(salbotm, k = k_i[[i]]["k_0", 
##     "Sal"]) + s(year, bs = "re")
## 
## Parametric coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  1.98299    0.05754   34.46   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Approximate significance of smooth terms:
##                         edf Ref.df      F  p-value    
## s(startlon,startlat) 55.246 64.190  4.295  < 2e-16 ***
## s(tempbotm)           3.630  4.446 20.114  < 2e-16 ***
## s(turbbotm)           2.316  2.868  5.759 0.000774 ***
## s(salbotm)            1.684  2.036  1.918 0.140470    
## s(year)              10.479 16.000  1.434 0.004015 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## R-sq.(adj) =    0.4   Deviance explained = 56.6%
## GCV = 0.61425  Scale est. = 0.54348   n = 610
par(mfrow = c(2,2))

gam.check(sn.top.s2_FA)

## 
## Method: GCV   Optimizer: outer newton
## full convergence after 6 iterations.
## Gradient range [-9.648362e-11,1.972614e-09]
## (score 0.6142498 & scale 0.5434823).
## Hessian positive definite, eigenvalue range [0.0005504977,0.01122271].
## Model rank =  103 / 103 
## 
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
## 
##                         k'   edf k-index p-value
## s(startlon,startlat) 69.00 55.25    0.97    0.72
## s(tempbotm)           6.00  3.63    0.96    0.60
## s(turbbotm)           5.00  2.32    0.99    0.76
## s(salbotm)            5.00  1.68    0.93    0.34
## s(year)              17.00 10.48      NA      NA
par(mfrow = c(1,1))

for(i in 2:5 ){
  
  plot( sn.top.s2_FA,
       select = i)
  abline(h = 0, col = "red", lty = 3)

  }

Spinner

Failed to Fit

sp_s2_FA$results

sp.top.s2_FA <- sp_s2_FA$model_fits

summary(sp.top.s2_FA)

Tiger

Failed to Fit

ti_s2_FA$results

ti.top.s2_FA <- ti_s2_FA$model_fits

summary(ti.top.s2_FA)

Juvenile

#### Strata 2 ####
  ## Female Juvenile
filenames_s2_FJ <- list.files("output/output_GAM/strat2/Female_Juvenile", pattern="*.rda", full.names=TRUE)

for( i in 1:length( filenames_s2_FJ ) ){ load( file = filenames_s2_FJ[ i ] ) }

rm( filenames_s2_FJ )

Blacknose

bn_s2_FJ$results
bn.top.s2_FJ <- bn_s2_FJ$model_fits$Lat_Lon_Temp_Turb_Oxy_Sal_Bottm

summary(bn.top.s2_FJ)
## 
## Family: Gamma 
## Link function: log 
## 
## Formula:
## (CPUE + 1) ~ s(startlon, startlat, k = (k_i[[i]]["k_0", "Lat_Lon"] + 
##     35)) + s(tempbotm, k = k_i[[i]]["k_0", "Temp"]) + s(turbbotm, 
##     k = k_i[[i]]["k_0", "Turb"]) + s(oxybotm, k = k_i[[i]]["k_0", 
##     "Oxy"]) + s(salbotm, k = k_i[[i]]["k_0", "Sal"]) + bottype4 + 
##     s(year, bs = "re")
## 
## Parametric coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   0.88454    0.08182  10.811   <2e-16 ***
## bottype4Mud   0.13801    0.13221   1.044    0.298    
## bottype4Rock  0.19935    0.16492   1.209    0.229    
## bottype4Sand  0.01249    0.08657   0.144    0.885    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Approximate significance of smooth terms:
##                         edf Ref.df     F p-value  
## s(startlon,startlat) 37.630 44.495 1.609  0.0204 *
## s(tempbotm)           1.000  1.000 4.818  0.0296 *
## s(turbbotm)           1.000  1.000 0.041  0.8395  
## s(oxybotm)            4.319  5.021 1.363  0.2412  
## s(salbotm)            1.000  1.000 1.565  0.2128  
## s(year)               1.063 16.000 0.058  0.5215  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## R-sq.(adj) =  0.246   Deviance explained = 52.4%
## GCV = 0.13709  Scale est. = 0.12237   n = 204
par(mfrow = c(2,2))

gam.check(bn.top.s2_FJ)

## 
## Method: GCV   Optimizer: outer newton
## full convergence after 10 iterations.
## Gradient range [-7.685031e-08,2.956633e-08]
## (score 0.137088 & scale 0.1223676).
## Hessian positive definite, eigenvalue range [3.68159e-09,0.004954792].
## Model rank =  88 / 88 
## 
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
## 
##                         k'   edf k-index p-value
## s(startlon,startlat) 49.00 37.63    1.17    1.00
## s(tempbotm)           4.00  1.00    1.10    0.92
## s(turbbotm)           4.00  1.00    1.06    0.80
## s(oxybotm)            6.00  4.32    0.96    0.28
## s(salbotm)            4.00  1.00    1.08    0.87
## s(year)              17.00  1.06      NA      NA
par(mfrow = c(1,1))

for(i in 2:7 ){
  
  plot( bn.top.s2_FJ,
       select = i)
  abline(h = 0, col = "red", lty = 3)

  }

Blacktip

bt_s2_FJ$results
bt.top.s2_FJ <- bt_s2_FJ$model_fits$Lat_Lon_Root_Dep_Temp_Turb_Oxy_Sal

summary(bt.top.s2_FJ)
## 
## Family: Gamma 
## Link function: log 
## 
## Formula:
## (CPUE + 1) ~ s(startlon, startlat, k = (k_i[[i]]["k_0", "Lat_Lon"] + 
##     35)) + s(I(startdepth^0.25), k = k_i[[i]]["k_0", "Root_Dep"]) + 
##     s(tempbotm, k = k_i[[i]]["k_0", "Temp"]) + s(turbbotm, k = k_i[[i]]["k_0", 
##     "Turb"]) + s(oxybotm, k = k_i[[i]]["k_0", "Oxy"]) + s(salbotm, 
##     k = k_i[[i]]["k_0", "Sal"]) + s(year, bs = "re")
## 
## Parametric coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  1.17913    0.07139   16.52   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Approximate significance of smooth terms:
##                         edf Ref.df     F p-value  
## s(startlon,startlat)  6.237  8.249 0.633  0.7629  
## s(I(startdepth^0.25)) 2.478  3.064 3.319  0.0215 *
## s(tempbotm)           2.509  3.049 1.590  0.1896  
## s(turbbotm)           4.539  5.242 1.322  0.2167  
## s(oxybotm)            1.000  1.000 6.102  0.0147 *
## s(salbotm)            4.288  4.998 2.249  0.0542 .
## s(year)               8.617 16.000 0.876  0.0609 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## R-sq.(adj) =   0.36   Deviance explained = 55.2%
## GCV = 0.23892  Scale est. = 0.21357   n = 166
par(mfrow = c(2,2))

gam.check(bt.top.s2_FJ)

## 
## Method: GCV   Optimizer: outer newton
## full convergence after 9 iterations.
## Gradient range [-2.192482e-07,5.640084e-08]
## (score 0.2389236 & scale 0.2135671).
## Hessian positive definite, eigenvalue range [2.191957e-07,0.003961029].
## Model rank =  93 / 93 
## 
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
## 
##                          k'   edf k-index p-value
## s(startlon,startlat)  49.00  6.24    1.10    0.96
## s(I(startdepth^0.25))  5.00  2.48    1.10    0.92
## s(tempbotm)            5.00  2.51    0.99    0.54
## s(turbbotm)            6.00  4.54    1.07    0.83
## s(oxybotm)             4.00  1.00    1.10    0.94
## s(salbotm)             6.00  4.29    1.10    0.95
## s(year)               17.00  8.62      NA      NA
par(mfrow = c(1,1))

for(i in 2:7 ){
  
  plot( bt.top.s2_FJ,
       select = i)
  abline(h = 0, col = "red", lty = 3)

  }

Bull

FAILED To Fit

bu_s2_FJ$results

bu.top.s2_FJ <- bu_s2_FJ$model_fits

summary(bu.top.s2_FJ)

Sandbar

sb_s2_FJ$results

sb.top.s2_FJ <- sb_s2_FJ$model_fits$Lat_Lon_Turb_Dis

summary(sb.top.s2_FJ)

Sharpnose

sn_s2_FJ$results
sn.top.s2_FJ <- sn_s2_FJ$model_fits$Lat_Lon_Temp_Turb_Dis_Bottm

summary(sn.top.s2_FJ)
## 
## Family: Gamma 
## Link function: log 
## 
## Formula:
## (CPUE + 1) ~ s(startlon, startlat, k = (k_i[[i]]["k_0", "Lat_Lon"] + 
##     35)) + s(tempbotm, k = k_i[[i]]["k_0", "Temp"]) + s(turbbotm, 
##     k = k_i[[i]]["k_0", "Turb"]) + s(Dis.to.SHORE, k = k_i[[i]]["k_0", 
##     "Dis"]) + bottype4 + s(year, bs = "re")
## 
## Parametric coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   1.25803    0.13133   9.579   <2e-16 ***
## bottype4Mud  -0.04466    0.15326  -0.291   0.7709    
## bottype4Rock  0.26242    0.22998   1.141   0.2548    
## bottype4Sand -0.25355    0.13345  -1.900   0.0585 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Approximate significance of smooth terms:
##                        edf Ref.df     F  p-value    
## s(startlon,startlat) 5.417  7.398 1.091 0.361098    
## s(tempbotm)          3.394  4.151 4.654 0.000986 ***
## s(turbbotm)          5.887  6.590 2.332 0.035449 *  
## s(Dis.to.SHORE)      1.000  1.000 4.406 0.036688 *  
## s(year)              2.573 16.000 0.153 0.471915    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## R-sq.(adj) =  0.107   Deviance explained = 24.2%
## GCV = 0.24652  Scale est. = 0.30855   n = 297
par(mfrow = c(2,2))

gam.check(sn.top.s2_FJ)

## 
## Method: GCV   Optimizer: outer newton
## full convergence after 10 iterations.
## Gradient range [-1.696181e-07,4.29269e-08]
## (score 0.2465193 & scale 0.308553).
## Hessian positive definite, eigenvalue range [1.695655e-07,0.0009957643].
## Model rank =  92 / 92 
## 
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
## 
##                         k'   edf k-index p-value
## s(startlon,startlat) 54.00  5.42    1.06    0.93
## s(tempbotm)           6.00  3.39    1.01    0.63
## s(turbbotm)           7.00  5.89    0.97    0.41
## s(Dis.to.SHORE)       4.00  1.00    1.00    0.60
## s(year)              17.00  2.57      NA      NA
par(mfrow = c(1,1))

for(i in 2:6 ){
  
  plot( sn.top.s2_FJ,
       select = i)
  abline(h = 0, col = "red", lty = 3)

  }

Spinner

sp_s2_FJ$results

sp.top.s2_FJ <- sp_s2_FJ$model_fits$Lat_Lon_Temp_Turb_Sal_Dis

summary(sp.top.s2_FJ)

Tiger

ti_s2_FJ$results

ti.top.s2_FJ <- ti_s2_FJ$model_fits$Lat_Lon_Temp_Turb_Dis_Bottm

summary(ti.top.s2_FJ)

Male

Adult

#### Strata 2 ####
  ## Male Adult
filenames_s2_MA <- list.files("output/output_GAM/strat2/Male_Adult", pattern="*.rda", full.names=TRUE)

for( i in 1:length( filenames_s2_MA ) ){ load( file = filenames_s2_MA[ i ] ) }

rm( filenames_s2_MA )
load("output/output_GAM/strat2/Male_Adult/bn_s2_MA.rda")
load("output/output_GAM/strat2/Male_Adult/bt_s2_MA.rda")
load("output/output_GAM/strat2/Male_Adult/sn_s2_MA.rda")

Blacknose

bn_s2_MA$results
bn.top.s2_MA <- bn_s2_MA$model_fits$Lat_Lon_Temp_Turb_Oxy_Sal_Bottm

summary(bn.top.s2_MA)
## 
## Family: Gamma 
## Link function: log 
## 
## Formula:
## (CPUE + 1) ~ s(startlon, startlat, k = (k_i[[i]]["k_0", "Lat_Lon"] + 
##     35)) + s(tempbotm, k = k_i[[i]]["k_0", "Temp"]) + s(turbbotm, 
##     k = k_i[[i]]["k_0", "Turb"]) + s(oxybotm, k = k_i[[i]]["k_0", 
##     "Oxy"]) + s(salbotm, k = k_i[[i]]["k_0", "Sal"]) + bottype4 + 
##     s(year, bs = "re")
## 
## Parametric coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   1.16985    0.13864   8.438 2.88e-15 ***
## bottype4Mud  -0.06725    0.16838  -0.399     0.69    
## bottype4Rock  0.34099    0.28929   1.179     0.24    
## bottype4Sand  0.17541    0.13970   1.256     0.21    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Approximate significance of smooth terms:
##                         edf Ref.df     F p-value  
## s(startlon,startlat) 26.777 34.938 1.260  0.1672  
## s(tempbotm)           1.849  2.254 1.019  0.3359  
## s(turbbotm)           1.003  1.005 0.016  0.8978  
## s(oxybotm)            4.203  5.035 1.921  0.0948 .
## s(salbotm)            2.829  3.428 3.246  0.0177 *
## s(year)               6.293 16.000 0.480  0.2104  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## R-sq.(adj) =  0.168   Deviance explained = 44.1%
## GCV = 0.31712  Scale est. = 0.32521   n = 291
par(mfrow = c(2,2))

gam.check(bn.top.s2_MA)

## 
## Method: GCV   Optimizer: outer newton
## full convergence after 7 iterations.
## Gradient range [-1.287003e-06,8.769384e-07]
## (score 0.3171185 & scale 0.325213).
## Hessian positive definite, eigenvalue range [3.582848e-07,0.004886936].
## Model rank =  95 / 95 
## 
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
## 
##                         k'   edf k-index p-value  
## s(startlon,startlat) 54.00 26.78    1.06    0.96  
## s(tempbotm)           4.00  1.85    0.89    0.07 .
## s(turbbotm)           4.00  1.00    1.07    0.94  
## s(oxybotm)            7.00  4.20    1.00    0.66  
## s(salbotm)            5.00  2.83    0.97    0.49  
## s(year)              17.00  6.29      NA      NA  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
par(mfrow = c(1,1))

for(i in 2:7 ){
  
  plot( bn.top.s2_MA,
       select = i)
  abline(h = 0, col = "red", lty = 3)

  }

Blacktip

bt_s2_MA$results
bt.top.s2_MA <- bt_s2_MA$model_fits$Lat_Lon_Root_Dep_Temp_Turb_Sal_Dis

summary(bt.top.s2_MA)
## 
## Family: Gamma 
## Link function: log 
## 
## Formula:
## (CPUE + 1) ~ s(startlon, startlat, k = (k_i[[i]]["k_0", "Lat_Lon"] + 
##     35)) + s(I(startdepth^0.25), k = k_i[[i]]["k_0", "Root_Dep"]) + 
##     s(tempbotm, k = k_i[[i]]["k_0", "Temp"]) + s(turbbotm, k = k_i[[i]]["k_0", 
##     "Turb"]) + s(salbotm, k = k_i[[i]]["k_0", "Sal"]) + s(Dis.to.SHORE, 
##     k = k_i[[i]]["k_0", "Dis"]) + s(year, bs = "re")
## 
## Parametric coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.96141    0.05074   18.95   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Approximate significance of smooth terms:
##                         edf Ref.df     F p-value  
## s(startlon,startlat)  5.462  7.313 1.429  0.1814  
## s(I(startdepth^0.25)) 1.173  1.315 3.047  0.0528 .
## s(tempbotm)           1.000  1.000 3.291  0.0714 .
## s(turbbotm)           1.594  1.950 1.045  0.3013  
## s(salbotm)            1.000  1.000 4.433  0.0367 *
## s(Dis.to.SHORE)       1.000  1.000 5.031  0.0262 *
## s(year)               8.162 16.000 0.867  0.0472 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## R-sq.(adj) =  0.134   Deviance explained = 32.8%
## GCV = 0.15903  Scale est. = 0.19363   n = 187
par(mfrow = c(2,2))

gam.check(bt.top.s2_MA)

## 
## Method: GCV   Optimizer: outer newton
## full convergence after 11 iterations.
## Gradient range [-1.112487e-07,2.165706e-08]
## (score 0.1590312 & scale 0.1936253).
## Hessian positive definite, eigenvalue range [1.259283e-08,0.003886915].
## Model rank =  82 / 82 
## 
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
## 
##                          k'   edf k-index p-value
## s(startlon,startlat)  44.00  5.46    1.16    1.00
## s(I(startdepth^0.25))  4.00  1.17    1.06    0.82
## s(tempbotm)            4.00  1.00    0.95    0.24
## s(turbbotm)            4.00  1.59    0.99    0.49
## s(salbotm)             4.00  1.00    0.98    0.40
## s(Dis.to.SHORE)        4.00  1.00    1.05    0.82
## s(year)               17.00  8.16      NA      NA
par(mfrow = c(1,1))

for(i in 2:7 ){
  
  plot( bt.top.s2_MA,
       select = i)
  abline(h = 0, col = "red", lty = 3)

  }

Bull

FAILED To Fit

bu_s2_MA$results

bu.top.s2_MA <- bu_s2_MA$model_fits

summary(bu.top.s2_MA)

Sandbar

FAILED To Fit

sb_s2_MA$results

sb.top.s2_MA <- sb_s2_MA$model_fits

summary(sb.top.s2_MA)

Sharpnose

sn_s2_MA$results
sn.top.s2_MA <- sn_s2_MA$model_fits$Lat_Lon_Root_Dep_Turb_Oxy_Sal_Dis_Bottm

summary(sn.top.s2_MA)
## 
## Family: Gamma 
## Link function: log 
## 
## Formula:
## (CPUE + 1) ~ s(startlon, startlat, k = (k_i[[i]]["k_0", "Lat_Lon"] + 
##     35)) + s(I(startdepth^0.25), k = k_i[[i]]["k_0", "Root_Dep"]) + 
##     s(turbbotm, k = k_i[[i]]["k_0", "Turb"]) + s(oxybotm, k = k_i[[i]]["k_0", 
##     "Oxy"]) + s(salbotm, k = k_i[[i]]["k_0", "Sal"]) + s(Dis.to.SHORE, 
##     k = k_i[[i]]["k_0", "Dis"]) + bottype4 + s(year, bs = "re")
## 
## Parametric coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   1.66249    0.13544  12.274   <2e-16 ***
## bottype4Mud   0.22320    0.14529   1.536    0.125    
## bottype4Rock -0.24318    0.18714  -1.299    0.194    
## bottype4Sand -0.02407    0.12533  -0.192    0.848    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Approximate significance of smooth terms:
##                          edf Ref.df     F  p-value    
## s(startlon,startlat)  30.988 37.544 2.528 2.77e-06 ***
## s(I(startdepth^0.25))  3.653  4.306 5.019 0.000334 ***
## s(turbbotm)            1.002  1.004 4.767 0.029176 *  
## s(oxybotm)             1.512  1.858 0.692 0.397493    
## s(salbotm)             3.922  4.512 2.394 0.044119 *  
## s(Dis.to.SHORE)        1.964  2.366 1.357 0.272375    
## s(year)               11.215 16.000 1.938 0.000219 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## R-sq.(adj) =  0.154   Deviance explained =   35%
## GCV = 0.52138  Scale est. = 0.57757   n = 834
par(mfrow = c(2,2))

gam.check(sn.top.s2_MA)

## 
## Method: GCV   Optimizer: outer newton
## full convergence after 7 iterations.
## Gradient range [-5.613582e-07,5.115974e-09]
## (score 0.5213762 & scale 0.5775706).
## Hessian positive definite, eigenvalue range [5.599824e-07,0.005746187].
## Model rank =  87 / 87 
## 
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
## 
##                          k'   edf k-index p-value  
## s(startlon,startlat)  44.00 30.99    0.96   0.495  
## s(I(startdepth^0.25))  5.00  3.65    0.93   0.250  
## s(turbbotm)            4.00  1.00    0.95   0.495  
## s(oxybotm)             4.00  1.51    0.94   0.340  
## s(salbotm)             5.00  3.92    1.01   0.980  
## s(Dis.to.SHORE)        4.00  1.96    0.91   0.075 .
## s(year)               17.00 11.22      NA      NA  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
par(mfrow = c(1,1))

for(i in 2:8 ){
  
  plot( sn.top.s2_MA,
       select = i)
  abline(h = 0, col = "red", lty = 3)

  }

Spinner

FAILED To Fit

sp_s2_MA$results

sp.top.s2_MA <- sp_s2_MA$model_fits

summary(sp.top.s2_MA)

Tiger

FAILED To Fit

ti_s2_MA$results

ti.top.s2_MA <- ti_s2_MA$model_fits

summary(ti.top.s2_MA)

Juvenile

#### Strata 2 ####
  ## Male Juvenile
filenames_s2_MJ <- list.files("output/output_GAM/strat2/Male_Juvenile", pattern="*.rda", full.names=TRUE)

for( i in 1:length( filenames_s2_MJ ) ){ load( file = filenames_s2_MJ[ i ] ) }

rm( filenames_s2_MJ )

Blacknose

bn_s2_MJ$results
bn.top.s2_MJ <- bn_s2_MJ$model_fits$Lat_Lon_Root_Dep_Temp_Turb_Oxy_Sal_Bottyp

summary(bn.top.s2_MJ)
## 
## Family: Gamma 
## Link function: log 
## 
## Formula:
## (CPUE + 1) ~ s(startlon, startlat, k = (k_i[[i]]["k_0", "Lat_Lon"] + 
##     35)) + s(I(startdepth^0.25), k = k_i[[i]]["k_0", "Root_Dep"]) + 
##     s(tempbotm, k = k_i[[i]]["k_0", "Temp"]) + s(turbbotm, k = k_i[[i]]["k_0", 
##     "Turb"]) + s(oxybotm, k = k_i[[i]]["k_0", "Oxy"]) + s(salbotm, 
##     k = k_i[[i]]["k_0", "Sal"]) + bottype4 + s(year, bs = "re")
## 
## Parametric coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   0.84278    0.10185   8.275 2.32e-14 ***
## bottype4Mud   0.09675    0.14306   0.676    0.500    
## bottype4Rock  0.22650    0.17252   1.313    0.191    
## bottype4Sand  0.14361    0.10638   1.350    0.179    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Approximate significance of smooth terms:
##                             edf Ref.df     F p-value
## s(startlon,startlat)  28.502438 35.323 0.930   0.528
## s(I(startdepth^0.25))  2.304337  2.750 1.422   0.324
## s(tempbotm)            1.662937  2.029 0.668   0.530
## s(turbbotm)            4.114370  4.633 1.618   0.125
## s(oxybotm)             3.944565  4.491 1.411   0.240
## s(salbotm)             1.548010  1.855 1.930   0.208
## s(year)                0.000197 16.000 0.000   0.867
## 
## R-sq.(adj) =  0.103   Deviance explained = 36.7%
## GCV = 0.16605  Scale est. = 0.17122   n = 234
par(mfrow = c(2,2))

gam.check(bn.top.s2_MJ)

## 
## Method: GCV   Optimizer: outer newton
## full convergence after 11 iterations.
## Gradient range [-1.142392e-07,1.664305e-09]
## (score 0.1660527 & scale 0.1712205).
## Hessian positive definite, eigenvalue range [1.142379e-07,0.00426818].
## Model rank =  87 / 87 
## 
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
## 
##                             k'      edf k-index p-value    
## s(startlon,startlat)  4.40e+01 2.85e+01    1.09    0.97    
## s(I(startdepth^0.25)) 4.00e+00 2.30e+00    1.02    0.59    
## s(tempbotm)           4.00e+00 1.66e+00    0.95    0.24    
## s(turbbotm)           5.00e+00 4.11e+00    0.99    0.50    
## s(oxybotm)            5.00e+00 3.94e+00    1.01    0.68    
## s(salbotm)            4.00e+00 1.55e+00    0.82  <2e-16 ***
## s(year)               1.70e+01 1.97e-04      NA      NA    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
par(mfrow = c(1,1))

for(i in 2:8 ){
  
  plot( bn.top.s2_MJ,
       select = i)
  abline(h = 0, col = "red", lty = 3)

  }

Blacktip

bt_s2_MJ$results
bt.top.s2_MJ <- bt_s2_MJ$model_fits$Lat_Lon_Root_Dep_Turb_Oxy_Sal

summary(bt.top.s2_MJ)
## 
## Family: Gamma 
## Link function: log 
## 
## Formula:
## (CPUE + 1) ~ s(startlon, startlat, k = (k_i[[i]]["k_0", "Lat_Lon"] + 
##     35)) + s(I(startdepth^0.25), k = k_i[[i]]["k_0", "Root_Dep"]) + 
##     s(turbbotm, k = k_i[[i]]["k_0", "Turb"]) + s(oxybotm, k = k_i[[i]]["k_0", 
##     "Oxy"]) + s(salbotm, k = k_i[[i]]["k_0", "Sal"]) + s(year, 
##     bs = "re")
## 
## Parametric coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  1.04583    0.09563   10.94   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Approximate significance of smooth terms:
##                          edf Ref.df     F p-value   
## s(startlon,startlat)   7.872 10.488 1.291 0.24117   
## s(I(startdepth^0.25))  2.854  3.477 1.877 0.10595   
## s(turbbotm)            1.000  1.000 1.501 0.22288   
## s(oxybotm)             5.688  6.584 2.532 0.01597 * 
## s(salbotm)             4.667  5.572 2.979 0.00988 **
## s(year)               12.247 16.000 1.349 0.03198 * 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## R-sq.(adj) =  0.396   Deviance explained = 61.6%
## GCV = 0.16883  Scale est. = 0.14052   n = 153
par(mfrow = c(2,2))

gam.check(bt.top.s2_MJ)

## 
## Method: GCV   Optimizer: outer newton
## full convergence after 12 iterations.
## Gradient range [-1.394889e-07,1.473762e-09]
## (score 0.1688277 & scale 0.1405247).
## Hessian positive definite, eigenvalue range [1.394785e-07,0.002283078].
## Model rank =  106 / 106 
## 
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
## 
##                          k'   edf k-index p-value
## s(startlon,startlat)  64.00  7.87    1.14    0.99
## s(I(startdepth^0.25))  5.00  2.85    1.07    0.81
## s(turbbotm)            4.00  1.00    1.07    0.84
## s(oxybotm)             8.00  5.69    0.94    0.27
## s(salbotm)             7.00  4.67    1.13    0.93
## s(year)               17.00 12.25      NA      NA
par(mfrow = c(1,1))

for(i in 2:5 ){
  
  plot( bt.top.s2_MJ,
       select = i)
  abline(h = 0, col = "red", lty = 3)

  }

Bull

Failed to fit

bu_s2_MJ$results

bu.top.s2_MJ <- bu_s2_MJ$model_fits

summary(bu.top.s2_MJ)

Sandbar

sb_s2_MJ$results

sb.top.s2_MJ <- sb_s2_MJ$model_fits$Lat_Lon_Turb_Sal

summary(sb.top.s2_MJ)

Sharpnose

sn_s2_MJ$results
sn.top.s2_MJ <- sn_s2_MJ$model_fits$Lat_Lon_Temp_Turb_Oxy_Sal

summary(sn.top.s2_MJ)
## 
## Family: Gamma 
## Link function: log 
## 
## Formula:
## (CPUE + 1) ~ s(startlon, startlat, k = (k_i[[i]]["k_0", "Lat_Lon"] + 
##     35)) + s(tempbotm, k = k_i[[i]]["k_0", "Temp"]) + s(turbbotm, 
##     k = k_i[[i]]["k_0", "Turb"]) + s(oxybotm, k = k_i[[i]]["k_0", 
##     "Oxy"]) + s(salbotm, k = k_i[[i]]["k_0", "Sal"]) + s(year, 
##     bs = "re")
## 
## Parametric coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  1.04658    0.02514   41.62   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Approximate significance of smooth terms:
##                            edf Ref.df      F  p-value    
## s(startlon,startlat) 24.550429 31.657  1.524 0.041374 *  
## s(tempbotm)           1.000002  1.000 12.500 0.000482 ***
## s(turbbotm)           1.000006  1.000  0.362 0.548069    
## s(oxybotm)            3.036418  3.701  2.608 0.053226 .  
## s(salbotm)            3.082509  3.712  0.913 0.368076    
## s(year)               0.000301 16.000  0.000 0.698354    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## R-sq.(adj) =   0.17   Deviance explained = 34.6%
## GCV = 0.16619  Scale est. = 0.18017   n = 285
par(mfrow = c(2,2))

gam.check(sn.top.s2_MJ)

## 
## Method: GCV   Optimizer: outer newton
## full convergence after 12 iterations.
## Gradient range [-6.775873e-08,1.218955e-09]
## (score 0.1661915 & scale 0.1801699).
## Hessian positive definite, eigenvalue range [1.759583e-09,0.002852139].
## Model rank =  80 / 80 
## 
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
## 
##                            k'      edf k-index p-value
## s(startlon,startlat) 4.40e+01 2.46e+01    1.13    1.00
## s(tempbotm)          4.00e+00 1.00e+00    0.99    0.58
## s(turbbotm)          4.00e+00 1.00e+00    0.95    0.20
## s(oxybotm)           5.00e+00 3.04e+00    1.01    0.68
## s(salbotm)           5.00e+00 3.08e+00    1.01    0.56
## s(year)              1.70e+01 3.01e-04      NA      NA
par(mfrow = c(1,1))

for(i in 2:6 ){
  
  plot( sn.top.s2_MJ,
       select = i)
  abline(h = 0, col = "red", lty = 3)

  }

Spinner

sp_s2_MJ$results ### top 11 dAIC < 4
### Lat_Lon in Top 2

sp.top.s2_MJ <- sp_s2_MJ$model_fits$Root_Dep_Temp_Turb_Oxy_Sal

summary(sp.top.s2_MJ)

sp.top2.s2_MJ <- sp_s2_MJ$model_fits$Lat_Lon_Root_Dep_Temp_Turb_Oxy_Sal

summary(sp.top2.s2_MJ)

Tiger

ti_s2_MJ$results ### Top 110 dAIC < 4

### Lat_Lon in Top 57

ti.top.s2_MJ <- ti_s2_MJ$model_fits$Temp_Oxy_Dis_Bottyp

summary(ti.top.s2_MJ)


ti.top57.s2_MJ <- ti_s2_MJ$model_fits$Lat_Lon_Oxy_Dis_Bottyp

summary(ti.top57.s2_MJ)